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Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22934
Abstract: A new class of poisoning attacks has recently emerged targeting the client‐side Domain Name System (DNS) cache. It allows users to visit fake websites unconsciously, thereby revealing their information, such as passwords. However, the current…
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Keywords:
proactive defense;
deep reinforcement;
client side;
defense ... See more keywords
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Published in 2021 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-021-03312-8
Abstract: Remote deep learning paradigm raises important privacy concerns related to clients sensitive data and deep learning models. However, dealing with such concerns may come at the expense of more client-side overhead, which does not fit…
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Keywords:
privacy;
constrained client;
client side;
deep learning ... See more keywords
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Published in 2024 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btae018
Abstract: Abstract Motivation The genomic surveillance of viral pathogens such as SARS-CoV-2 and HIV-1 has been critical to modern epidemiology and public health, but the use of sequence analysis pipelines requires computational expertise, and web-based platforms…
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Keywords:
web application;
suite viral;
viral genomics;
client side ... See more keywords
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Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3633695
Abstract: Mobile applications generate DNS queries that expose user behavioral patterns to network observers, creating privacy vulnerabilities even when communications are encrypted. Network eavesdroppers and DNS resolvers can analyze domain name sequences to profile users based…
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Keywords:
client side;
traffic;
query forgery;
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1
Published in 2022 at "IEEE Communications Magazine"
DOI: 10.1109/mcom.005.210108
Abstract: Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively training models at the network edge in a privacy-preserving fashion, without training data leaving the devices where it was generated. The privacy-preserving…
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Keywords:
federated learning;
side optimization;
client side;
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Published in 2021 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2020.2986205
Abstract: Collaborative learning allows multiple clients to train a joint model without sharing their data with each other. Each client performs training locally and then submits the model updates to a central server for aggregation. Since…
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Keywords:
detection;
client side;
poisoning attacks;
collaborative learning ... See more keywords
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1
Published in 2017 at "IEEE Transactions on Software Engineering"
DOI: 10.1109/tse.2016.2586066
Abstract: Client-side JavaScript is widely used in web applications to improve user-interactivity and minimize client-server communications. Unfortunately, JavaScript is known to be error-prone. While prior studies have demonstrated the prevalence of JavaScript faults, no attempts have…
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Keywords:
client side;
javascript faults;
causes consequences;
side javascript ... See more keywords
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Published in 2023 at "Studies in health technology and informatics"
DOI: 10.3233/shti230325
Abstract: Deep learning models for radiology are typically deployed either through cloud-based platforms, through on-premises infrastructures, or though heavyweight viewers. This tends to restrict the audience of deep learning models to radiologists working in state-of-the-art hospitals,…
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Keywords:
client side;
learning models;
application deep;
deep learning ... See more keywords
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1
Published in 2021 at "Hydrology"
DOI: 10.3390/hydrology8020065
Abstract: The height above nearest drainage (HAND) model is frequently used to calculate properties of the soil as well as predict flood inundation extents. HAND is extremely useful due to its lack of reliance on prior…
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Keywords:
flood mapping;
client side;
hand;
hand model ... See more keywords